Zobraziť v EDS

Streamflow Prediction in Poorly Gauged Watersheds in the United States Through Data‐Driven Sparse Sensing.

Uložené v:
Podrobná bibliografia
Názov: Streamflow Prediction in Poorly Gauged Watersheds in the United States Through Data‐Driven Sparse Sensing.
Autori: Zhang, Kun, Luhar, Mitul, Brunner, Manuela I., Parolari, Anthony J.
Zdroj: Water Resources Research; Apr2023, Vol. 59 Issue 4, p1-17, 17p
Predmety: STREAMFLOW, STREAM measurements, WATERSHEDS, GAGING, WATERSHED management, SENSOR placement, FEATURE extraction, BASE flow (Hydrology)
Geografický termín: ROCKY Mountains, NEW England, UNITED States
Abstrakt: Many rivers and streams are ungauged or poorly gauged and predicting streamflow in such watersheds is challenging. Although streamflow signals result from processes with different frequencies, they can be "sparse" or have a "lower‐dimensional" representation in a transformed feature space. In such cases, if this appropriate feature space can be identified from streamflow data in gauged watersheds by dimensionality reduction, streamflow in poorly gauged watersheds can be predicted with a few measurements taken. This study utilized this framework, named data‐driven sparse sensing (DSS), to predict daily‐scale streamflow in 543 watersheds across the contiguous United States. A tailored library of features was extracted from streamflow training data in watersheds within the same climatic region, and this feature space was used to reconstruct streamflow in poorly gauged watersheds and identify the optimal timings for measurement. Among different regions, streamflow in snowmelt‐dominated and baseflow‐dominated watersheds (e.g., Rocky Mountains) was more effectively predicted with fewer streamflow measurements taken. The prediction efficiency in some rainfall‐dominated regions, for example, New England and the Pacific coast, increased significantly with an increasing number of measurements. The spatial variability of prediction efficiency can be attributed to the process‐driven mechanisms and the dimensionality of watershed dynamics. Storage‐dominated systems are lower‐dimensional and more predictable than rainfall‐dominated systems. Measurements taken during periods with large streamflow magnitudes and/or variances are more informative and lead to better predictions. This study demonstrates that DSS can be an especially useful technique to integrate ground‐based measurements with remotely sensed data for streamflow prediction, sensor placement, and watershed classification. Plain Language Summary: Many rivers and stream reaches are ungauged or poorly gauged because streamflow measurement is costly and resource intensive. Predicting the streamflow time‐series in these ungauged or poorly gauged watersheds is still challenging. Here, we use a signal processing technique called data‐driven sparse sensing on a national‐scale streamflow data set across the contiguous United States. We predict streamflow time‐series in each watershed based on existing streamflow data in watersheds nearby, and explore the best times during the year for measuring streamflow. Our analysis shows that data‐driven sparse sensing is an effective tool to predict streamflow time‐series in poorly gauged watersheds based on very few streamflow measurements. The streamflow in watersheds with high snowmelt and high baseflow can be more easily predicted than in other watersheds. Our analysis also shows that the streamflow measurements taken during periods with large streamflow peaks and variances contain more information and are beneficial for making predictions. We conclude that data‐driven sparse sensing can be further used to classify watersheds and to identify the best locations for streamflow gauging. Key Points: We utilize data‐driven sparse sensing to predict daily streamflow and identify the optimal times for streamflow measurement across the contiguous United StatesStreamflow was more effectively predicted in watersheds dominated by snowmelt and baseflow than those dominated by rainfall and quickflowThe optimal sampling times for streamflow prediction by data‐driven sparse sensing are periods with large flow magnitudes and variances [ABSTRACT FROM AUTHOR]
Copyright of Water Resources Research is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Databáza: Biomedical Index
Popis
Abstrakt:Many rivers and streams are ungauged or poorly gauged and predicting streamflow in such watersheds is challenging. Although streamflow signals result from processes with different frequencies, they can be "sparse" or have a "lower‐dimensional" representation in a transformed feature space. In such cases, if this appropriate feature space can be identified from streamflow data in gauged watersheds by dimensionality reduction, streamflow in poorly gauged watersheds can be predicted with a few measurements taken. This study utilized this framework, named data‐driven sparse sensing (DSS), to predict daily‐scale streamflow in 543 watersheds across the contiguous United States. A tailored library of features was extracted from streamflow training data in watersheds within the same climatic region, and this feature space was used to reconstruct streamflow in poorly gauged watersheds and identify the optimal timings for measurement. Among different regions, streamflow in snowmelt‐dominated and baseflow‐dominated watersheds (e.g., Rocky Mountains) was more effectively predicted with fewer streamflow measurements taken. The prediction efficiency in some rainfall‐dominated regions, for example, New England and the Pacific coast, increased significantly with an increasing number of measurements. The spatial variability of prediction efficiency can be attributed to the process‐driven mechanisms and the dimensionality of watershed dynamics. Storage‐dominated systems are lower‐dimensional and more predictable than rainfall‐dominated systems. Measurements taken during periods with large streamflow magnitudes and/or variances are more informative and lead to better predictions. This study demonstrates that DSS can be an especially useful technique to integrate ground‐based measurements with remotely sensed data for streamflow prediction, sensor placement, and watershed classification. Plain Language Summary: Many rivers and stream reaches are ungauged or poorly gauged because streamflow measurement is costly and resource intensive. Predicting the streamflow time‐series in these ungauged or poorly gauged watersheds is still challenging. Here, we use a signal processing technique called data‐driven sparse sensing on a national‐scale streamflow data set across the contiguous United States. We predict streamflow time‐series in each watershed based on existing streamflow data in watersheds nearby, and explore the best times during the year for measuring streamflow. Our analysis shows that data‐driven sparse sensing is an effective tool to predict streamflow time‐series in poorly gauged watersheds based on very few streamflow measurements. The streamflow in watersheds with high snowmelt and high baseflow can be more easily predicted than in other watersheds. Our analysis also shows that the streamflow measurements taken during periods with large streamflow peaks and variances contain more information and are beneficial for making predictions. We conclude that data‐driven sparse sensing can be further used to classify watersheds and to identify the best locations for streamflow gauging. Key Points: We utilize data‐driven sparse sensing to predict daily streamflow and identify the optimal times for streamflow measurement across the contiguous United StatesStreamflow was more effectively predicted in watersheds dominated by snowmelt and baseflow than those dominated by rainfall and quickflowThe optimal sampling times for streamflow prediction by data‐driven sparse sensing are periods with large flow magnitudes and variances [ABSTRACT FROM AUTHOR]
ISSN:00431397
DOI:10.1029/2022WR034092